Overview

expoFitter is a Shiny-based application designed for the analysis and modeling of electrophysiological data, with a focus on current vs. time traces commonly encountered in neuroscience research. The app provides tools for data visualization, preprocessing, and fitting of both single- and double-exponential models. This facilitates the study of dynamic processes in neuronal systems, such as ionic currents, calcium dynamics, or neural network behavior.

The application includes backend functions for filtering data, computing coefficients of determination (R²), and fitting models using non-linear least squares optimization. Additionally, the app provides an intuitive, interactive interface for setting model parameters and visualizing fitted models.

Features

• Data Filtering: Employs finite differences and simple differences to filter noise from experimental data.
• Exponential Model Fitting: Supports single- and double-exponential model fitting using the nlsLM algorithm.
• Interactive Visualization: Visualizes raw data and fitted models with dynamic slider inputs to adjust fitting parameters.
• Flexible UI: User-friendly interface powered by R Shiny and flexdashboard for flexible, customizable data exploration.

Installation

To run the expoFitter application locally, ensure that R is installed on your system along with the following packages:

• dplyr
• minpack.lm
• DT
• shiny

Step-by-Step Instructions

1. Clone this repository to your local machine: bash git clone https://github.com/your-username/expoFitter.git

2. Navigate to the repository directory: bash cd expoFitter

3. Open R or RStudio and install the required packages: r install.packages(c('dplyr', 'minpack.lm', 'DT', 'shiny'))

4. Run the application: r shiny::runApp('path/to/repository')

Alternatively, you can deploy the app directly from RStudio using the Run App button.

Usage

Data Input

• Upload a .csv file containing time and current data. Ensure that the file has at least two columns:
  • Column 1: Time (in seconds)
  • Column 2: Current (in nanoamps)

Model Initialization

• Select Model: Choose between a ‘Single-Exponential,’ ‘Double-Exponential,’ or ‘Both’ models from the dropdown menu.
• Adjust Parameters: Use the sliders to set initial values for model parameters (A, B, C, etc.).

Visualization and Fitting

• Visualize Data: The app plots the input current vs. time data automatically.
• Fit Model: Click the ‘Fit Model’ button to apply the selected model to the data and visualize the fitted curve.

Saving Results

• Once the model fitting is complete, you can save the resulting plot as a .pdf file by clicking the ‘Save’ button in the interface.

Output

• Model parameters, R² values, and fitting summaries are displayed in the ‘Model Solution’ section, providing quantitative feedback on the model’s performance.

Contribution Guidelines

If you would like to contribute to expoFitter, please adhere to the following guidelines:

1. Fork the Repository: Start by forking this repository to your own GitHub account.

2. Clone Your Fork: Clone the forked repository to your local development environment. bash git clone https://github.com/your-username/expoFitter.git

3. Create a New Branch: Make your changes in a new branch. bash git checkout -b feature-name

4. Testing: Ensure that all new features are fully tested. Utilize mock datasets for validation of model fitting functionalities.

5. Submit a Pull Request: Once your changes are ready, submit a pull request with a clear description of your additions or modifications.

Code Style

• Follow the R language style guide: Google’s R Style Guide.
• Write clear and concise comments for each function, describing input parameters and return values.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

This application is inspired by Dr. Ellison’s research in computational neuroscience, focusing on the analysis of electrophysiological processes in motor control and neural activity. The design leverages Dr. Ellison’s expertise in both the computational and experimental realms of neuroscience.

Contact Information

For further questions or collaboration, please contact:

Ryan Ellison, PhD
Email | GitHub

Additional Resources

For more details on the scientific basis and applications of the expoFitter tool, please refer to Dr. Ellison’s publications and research in computational neuroscience and electrophysiological data analysis.